An Online Learning Framework for Energy-Efficient Navigation of Electric Vehicles
Authors: Niklas Åkerblom, Yuxin Chen, Morteza Haghir Chehreghani
IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we demonstrate the performance of our methods via several real-world experiments on Luxembourg SUMO Traffic dataset. 5 Experimental Results |
| Researcher Affiliation | Collaboration | Niklas Akerblom1,3 , Yuxin Chen2 and Morteza Haghir Chehreghani3 1Volvo Car Corporation 2The University of Chicago 3Chalmers University of Technology |
| Pseudocode | Yes | Algorithm 1 Online learning for energy-efficient navigation; Algorithm 2 Gaussian parameter update of the energy model; Algorithm 3 Thompson Sampling; Algorithm 4 Bayes UCB |
| Open Source Code | No | The paper does not provide any links to source code or state that its code is publicly available. |
| Open Datasets | Yes | We utilize the Luxembourg SUMO Traffic (Lu ST) Scenario data [Codec a et al., 2017] to provide realistic traffic patterns and vehicle speed distributions for each hour of the day. |
| Dataset Splits | No | The paper describes its online learning framework and experiments over a 'horizon' of sessions, but it does not specify traditional train/validation/test dataset splits or cross-validation details for reproducibility. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper mentions using 'Luxembourg SUMO Traffic (Lu ST) Scenario data' and extending a 'simulation framework' but does not specify any software dependencies with version numbers (e.g., Python, PyTorch, SUMO versions). |
| Experiment Setup | Yes | We use the default vehicle parameters that were provided for the energy consumption model in [Basso et al., 2019], with vehicle frontal surface area A = 8 meters, air drag coefficient Cd = 0.7 and rolling resistance coefficient Cr = 0.0064. The vehicle is a medium duty truck with vehicle mass m = 14750 kg... We set ϕ = 0.1 for both. For the prior... σ2 0 = (ϑµ0(e))2, where ϑ = 0.25. We run the simulations with a horizon of T = 400 (i.e., T = 400 sessions). |